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FedADC: Accelerated federated learning with drift control

Title	FedADC: Accelerated federated learning with drift control
Author	Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.
Publication Date:	2021
Publication Place	- IEEE
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-5386-8209-8
Record ID	4efdcd74-194b-4ad5-9c49-916da91eb333
Date	2021
Notes	Engineering and Physical Sciences Research Council ; European Research Council
Sample Text	Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.
DOI	10.1109/ISIT45174.2021.9517850

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FedADC: Accelerated federated learning with drift control

Author Özfatura, E., Özfatura, Ahmet Kerem, Gündüz, D.

Publication Date 2021

Publication Place - IEEE

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-5386-8209-8

Record ID 4efdcd74-194b-4ad5-9c49-916da91eb333

Date 2021

Notes Engineering and Physical Sciences Research Council ; European Research Council

Sample Text Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.

DOI 10.1109/ISIT45174.2021.9517850